README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - type: precision
            value: 0.9329581195166363
            name: Precision
          - type: recall
            value: 0.9485021878155503
            name: Recall
          - type: f1
            value: 0.9406659434198448
            name: F1
          - type: accuracy
            value: 0.985356449049273
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: conll2003
          type: conll2003
          config: conll2003
          split: test
        metrics:
          - type: accuracy
            value: 0.8998282409668931
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9289800450920349
            name: Precision
            verified: true
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          - type: recall
            value: 0.915425937593262
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: NaN
            name: AUC
            verified: true
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          - type: f1
            value: 0.9221531883622275
            name: F1
            verified: true
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          - type: loss
            value: 0.8484721779823303
            name: loss
            verified: true
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bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0649
• Precision: 0.9330
• Recall: 0.9485
• F1: 0.9407
• Accuracy: 0.9854

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0871	1.0	1756	0.0672	0.9209	0.9387	0.9297	0.9834
0.0394	2.0	3512	0.0584	0.9311	0.9505	0.9407	0.9857
0.0201	3.0	5268	0.0649	0.9330	0.9485	0.9407	0.9854

Framework versions

• Transformers 4.19.2
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1